Information retrieval systems are typically designed to retrieve relevant content from a data repository, based on inputs from users. The user input can be in any of the following example forms: (i) a set of keywords, (ii) single or multiple lists of URLs and domains, and (iii) a set of documents (e.g., text files, HTML pages, or other types of markup language content). A goal of such information retrieval systems is to pull the most relevant content (i.e., most relevant to the given input) from the underlying repository, which might itself consist of a heterogeneous set of structured and unstructured content. An example of the aforementioned information retrieval system is a traditional search engine, where a user provides a set of keywords, and the search engine provides simple ranked lists of top relevant web pages, and a separate list of top relevant paid listings or sponsored links. The set of web pages matching user's search queries and the advertisement database containing sponsored advertising materials are currently two separate databases that are processed very differently to pull the relevant pages and the sponsored links for the same user query. Thus, the conventional search engine described above provides an example of two distinct information repositories being processed in response to the same query.
Current systems find important keywords of a web page then try to expand them using various resources. This expanded set of keywords is compared with a user-provided set of keywords. One problem with such an approach is that keywords can have different meanings. For example, “Chihuahua” is a dog breed, but it is also a province in Mexico. In current systems, Chihuahua may expand to:    Chihuahua Breeders,    Travel to Chihuahua    Travel to Mexico    Chihuahua Puppy    Dog Training    Hotels in Chihuahua    Teacup Chihuahua Puppies    Cheap flights,    . . .
A person interested in a Chihuahua dog would find information about the Chihuahua province or travel to it less useful. And a person interested in the Chihuahua province would find information about dog training or a Chihuahua dog less useful. Without knowing the context of the user-provided set of keywords, current systems often present search results that are irrelevant to what the user is seeking.
While the aforementioned systems allow for limited targeting of advertisement and content, such systems fail to provide efficient targeted advertisement avenues. Accordingly, a need exists for an improved solution for advertisement targeting.